Dwelling Energy Scheduling Using Weather and Usage Forecasts
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Solution Overview
Problem
Existing systems for monitoring and optimizing energy consumption in residential units with renewable energy sources lack the ability to predict long-term energy availability, leading to inefficient use of renewable energy and increased reliance on grid energy.
Innovation Solution
A method that uses historical data and weather forecasts to generate a long-term schedule for activating and deactivating household appliances, maximizing energy consumption from renewable sources by determining optimal times for using energy produced from photovoltaic panels and other renewable sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and control logic is used to manage energy consumption, then immediate optimization of energy use is achieved, but the system cannot predict long-term energy availability or schedule consumptions in advance
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical energy production and consumption data to create predictive models. These models enable the system to forecast future renewable energy availability and pre-schedule appliance operations in advance, rather than reacting only to real-time conditions. This allows the system to proactively optimize energy usage based on predicted surplus periods.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy production and consumption, comparing these measurements against predicted values, and using the differences to refine predictive algorithms. This closed-loop feedback enables the system to improve its long-term prediction accuracy while maintaining real-time optimization capabilities.
2Loss of information
If historical data analysis is used to predict long-term energy availability, then future energy scheduling capability is improved, but real-time responsiveness may be reduced
Solution Approach 1:
The system segments its operation into distinct time-based layers: a strategic planning layer that uses historical data for long-term predictions and schedule generation, and a tactical execution layer that handles real-time monitoring and immediate adjustments. This segmentation allows each layer to operate at its optimal speed without compromising the other.
Solution Approach 2:
The system introduces an intermediary predictive modeling component that bridges historical data analysis and real-time control. This intermediary processes historical patterns to generate forecasts, which then guide real-time decisions without requiring the entire real-time system to perform complex historical analysis, thus maintaining responsiveness while improving forecast accuracy.
Data Source
AI summary
A method for optimizing consumption of electrical energy in a dwelling from renewable sources includes determining electrical energy consumption in the dwelling within a first time interval based on historical data; determining electrical energy production from the renewable sources in the first time interval based on historical data; entering, in an electronic control unit, one or more utilities present in the dwelling and for which to generate an activation and/or deactivation schedule in a second period of time after the first period of time; and generating the activation and/or deactivation schedule as a function of the historical data of energy consumption and production and forecasted weather data for the second time interval, so that the activation and/or deactivation schedule indicates, within the second time interval, a series of times and/or time sub-intervals distributed within the second time interval when to activate and/or deactivate one or more of the utilities.